Method for recognizing parking space for vehicle and parking assistance system using the method

ABSTRACT

A method for recognizing a parking space for a vehicle and a parking assistance system are disclosed. An obstacle is identified from successive image frames captured when the vehicle is moving and a first boundary for the obstacle is generated by a Convolutional Neural Network (CNN) algorithm based on a position of the obstacle shown in each of the successive image frames. Distances between the moving vehicle and the obstacle are detected by ultrasonic sensors. A second boundary for the obstacle is generated by a distance modification module based on the distances between the vehicle and the obstacle. A periphery of the obstacle is defined by a periphery definition module. In view of the periphery of the obstacle, a parking space is thus recognized by a parking space recognition module. The parking process can be changed to a self-drive mode, and remotely controlled by a mobile device.

FIELD OF THE INVENTION

The present invention relates to a method for recognizing a parkingspace for a vehicle and a parking assistance system using the method.More particularly, the present invention relates to a method forrecognizing a parking space for a vehicle by fusing image signals andultrasonic signals and a parking assistance system using the method.

BACKGROUND OF THE INVENTION

Thanks to the development of the automotive industry, the number ofautomobiles has been significantly increased and the automotivetechnologies have been aggressively developed. Particularly, because ofthe development of electronic technologies, many studies have been madewith respect to intelligent automotive technologies. Among them, driverassistance systems (DASs) have been remarkably improved and applied tointelligent automobiles in recent years.

Among the driver assistance systems, parking assistance systems havebeen also actively researched in the intelligent automotive field, andhave been practically applied to automobiles. Such parking assistancesystems are used for aiding a driver to park an automobile.

Such a parking assistance system may be divided into a parking spacerecognition module, a parking track generation module, and a steeringcontrol module. In addition, the parking space recognition module may bedivided into a parallel parking space recognition module and aperpendicular parking space recognition module. Parking spacerecognition may be performed, using ultrasonic waves, a rear-viewcamera, or a laser scanner.

Among the conventional parking space recognition methods, a parkingspace recognition method using ultrasonic waves has a problem in that aparking space cannot be precisely recognized since an edge of acounterpart vehicle adjacent to a parking space desired for parking avehicle cannot be precisely extracted due to the limitation in terms ofdistance and resolution of ultrasonic waves. A conventional parkingspace recognition method using a camera has a problem in that a parkingspace cannot be precisely recognized since the position of a counterpartvehicle cannot be precisely recognized by determining the distance tothe counterpart vehicle.

That is, the conventional parking space recognition methods using eitheran ultrasonic sensor or a camera for recognizing a parking space have aproblem in that they cannot precisely recognize a parking space due tothe disadvantages of the ultrasonic sensor and the camera, respectively.

As shown in FIG. 14, U.S. Pat. No. 7,272,477, “Vehicle Parking AssistingSystem and Method”, discloses a vehicle parking assisting system inwhich both of a sonic sensor and a camera are used to show a presentdetection point DP1 and a past detection point DP2 of an obstacle OB inan overlapping manner to assist vehicle parking. However, according tothe '477 Patent, the detection points of the obstacle are affected by alot of noises, thereby making recognition of a parking space inaccurate.

Referring to FIG. 15, U.S. Pat. No. 8,401,235, “Method and System forRecognizing Parking Lot”, also discloses a parking space recognitiontechnology using a camera and an ultrasonic sensor in such a manner thatthe disadvantages of the ultrasonic sensor and the camera can becompensated with each other. According to the '235 Patent, Sobel EdgeDetection Algorithm is used, which is relatively inaccurateapproximation though the computation is simple. The accuracy sufferssignificantly if no denoising process is carried out.

SUMMARY OF THE INVENTION

Accordingly, the present invention has been made to solve theabove-mentioned problems occurring in the prior arts, and an object ofthe present invention is to provide a technology for recognizing aparking space for a vehicle by fusing image signals and ultrasonicsignals.

According to an aspect of the present invention, there is provided amethod for recognizing a parking space for a vehicle, including thesteps of: capturing successive image frames containing an obstacle whilethe vehicle is moving; identifying the obstacle from the successiveimage frames and generating a first boundary for the obstacle by aConvolutional Neural Network (CNN) algorithm based on a position of theobstacle shown in each of the successive image frames; detecting aplurality of distances between the moving vehicle and the obstacle;generating a second boundary for the obstacle based on the plurality ofdistances between the vehicle and the obstacle; defining a periphery ofthe obstacle by adjusting the second boundary with the first boundary;and recognizing a parking space with the aid of the periphery of theobstacle.

According to another aspect of the present invention, there is provideda parking assistance system for a vehicle, including: an image capturemodule for capturing successive image frames containing an obstacle whenthe vehicle is moving; an identification module for identifying theobstacle from the successive image frames and generating a firstboundary for the obstacle by a Convolutional Neural Network (CNN)algorithm based on a position of the obstacle shown in each of thesuccessive image frames; at least an ultrasonic sensor for detecting aplurality of distances between the moving vehicle and the obstacle; adistance modification module for generating a second boundary for theobstacle based on the plurality of distances between the vehicle andobstacle; a periphery definition module for defining a periphery of theobstacle by adjusting the second boundary with the first boundary; and aparking space recognition module for recognizing a parking space withthe aid of the periphery of the obstacle.

This paragraph extracts and compiles some features of the presentinvention; other features will be disclosed in the follow-up paragraphs.It is intended to cover various modifications and similar arrangementsincluded within the spirit and scope of the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will be more apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram showing a parking assistance system for avehicle according to a preferred embodiment of the present invention;

FIG. 2 is a schematic plan view showing four cameras and six ultrasonicsensors mounted on a vehicle according to the preferred embodiment ofthe present invention;

FIG. 3 is a schematic view showing difference in recognition of aparking space by ultrasonic sensors and cameras;

FIG. 4 shows a picture image and its semantic segmentation;

FIG. 5 is a flowchart showing parking assistance procedures executed inthe preferred embodiment of the present invention;

FIGS. 6a and 6b are schematic views showing scanning of an obstacle by amoving vehicle according to the preferred embodiment of the presentinvention;

FIG. 7 is a schematic view showing unprocessed obstacle edge pointsobtained according to the preferred embodiment of the present invention;

FIGS. 8a-8d are schematic views showing merging and denoising of theobstacle edge points by multi-frame information according to thepreferred embodiment of the present invention;

FIG. 9 is a schematic view showing the processed obstacle edge pointswith merging and denoising by multi-frame information according to thepreferred embodiment of the present invention, in comparison with FIG.7;

FIG. 10 is a view illustrating refinement of a distance between themoving vehicle and the obstacle by fusion according to the preferredembodiment of the present invention;

FIGS. 11a-11c are views showing definition of a periphery of theobstacle by an iterated linear regression according to the preferredembodiment of the present invention;

FIG. 12 is a schematic view showing vehicle parking after a parkingspace is recognized according to the preferred embodiment of the presentinvention;

FIG. 13 is a schematic view showing the vehicle travelling from theparking space to a predetermined location according to the preferredembodiment of the present invention;

FIG. 14 is a schematic diagram illustrating a vehicle parking assistingsystem in which both of a sonic sensor and a camera are used in anoverlapping manner according to a prior art; and

FIG. 15 is a block diagram showing a parking space recognitiontechnology according to another prior art.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described more specifically withreference to the following embodiments.

Referring to FIG. 1, a preferred embodiment of a parking assistancesystem 1 for a subject vehicle SV (not shown in FIG. 1) according to thepresent invention is disclosed. The parking assistance system 1 has arecognition device 10, a parking control device 20, and a ControllerArea Network (CAN) bus communicated between the recognition device 10and the parking control device 20. The recognition device 10 includes animage capture module 100, a processing module 110, ultrasonic sensors120-125, and a display 130. The processing module 110 is programmed toreceive detection signals from the parking control device 20 via the CANbus and transmit control signals to the parking control device 20 viathe CAN bus. The parking control device 20 includes a steering wheelcontroller 200, a throttle controller 210, a braking controller 220 anda gear controller 230. Furthermore, a handheld device 30 such as a smartphone can be used to remotely control the parking assistance system 1 asshown.

The CAN bus is a vehicle bus standard designed to allow microprocessorsand devices to communicate with each other in applications without ahost computer and it is a message-based protocol for use in automobiles.One key advantage of the CAN bus is that interconnection betweendifferent vehicle systems can allow a wide range of safety, economy andconvenience features to be implemented using software alone. Otherwise,it would add cost and complexity if such features were “hard wired”using traditional automotive electrics. As a matter of fact allelectronic control units (ECUs) in a vehicle can be connected throughthe two-wired CAN bus.

In the present embodiment, in order to obtain panoramic survey of theenvironment around the subject vehicle SV, the image capture module 100includes four cameras 101-104 provided at the right rearview mirror, atthe left rearview mirror, above the rear license plate and above thefront license plate of the subject vehicle SV, respectively, as shown inFIG. 2. The ultrasonic sensors 120-125 are provided at the front leftend, front right end, rear left end, rear central parts, and rear rightend of the subject vehicle SV, respectively, as shown in FIG. 2.Although there are four cameras 101-104 included in the image capturemodule 100, it should be understood that the amount of cameras requiredis not limited to four. Similarly, the amount of ultrasonic sensors isnot limited to six. Ultrasonic waves are irradiated from the ultrasonicsensors 120-125 to form a generally fan-shaped detection area to detectan obstacle OB within the detection area close to a parking space.However, the length (SL) of a parking space recognized merely byultrasonic sensors are generally shorter than the real length (PL) of aparking space, as shown in FIG. 3, thereby causing a misjudgment ofwhether there's enough space for the subject vehicle SV to fit in theparking space. Specifically speaking, in order for the subject vehicleSV which has a length of L to fit in, the parking space needs to belarger than the length of the subject vehicle SV at least by 80 cm(i.e., PL≥L+80 cm). In order to accurately determine the length of aparking space, the present invention incorporated the use of the imagecapture module 100 to make up the insufficiency of ultrasonic sensors.Although ultrasonic sensors are able to precisely identify the distancebetween the obstacle OB and the subject vehicle SV and obtain boundariesA-C along the moving direction of the subject vehicle SV, ultrasonicsensors are not able to precisely identify boundaries D-E which arevertical to the moving direction of the subject vehicle SV. Furthermore,ultrasonic sensors have blind spots and are not able to identify thewhite line marks of the parking space, wheel stoppers, and lower objectson the ground (e.g., curbs), etc. However, such insufficiency ordeficiency of ultrasonic sensors could also be made up by the use of theimage capture module 100. Details will be described later.

The image capture module 100 sends the image picture frames captured bythe cameras 101-104 to the processing module 110. On the other hand, thedetection signals sent from the parking control device 20 to theprocessing module 110 through the CAN bus may include a speed detectionsignal indicative of the detected vehicle speed, a yaw rate detectionsignal indicative of the detected yaw rate, and a steering detectionsignal indicative of the detected rotating angle. Then, the processingmodule 110 determines a moving or turning condition of the vehicle SVbased on these detection signals.

The processing module 110 includes an identification module 1100, animage conversion module 1120, a distance modification module 1130, aperiphery definition module 1140, a parking space recognition module1150, a parking track module 1160, and a memory 1170. The identificationmodule 1110 in the processing module 110 identifies what the obstacle OBis, e.g., an adjacent vehicle, a lamppost, a wall, a curb, or even aparking space mark etc., and generates a first boundary B1 for theobstacle OB by a Semantic Segmentation Convolutional Neural Network(CNN) algorithm based on a position of the obstacle OB shown in each ofthe successive image picture frames. That is, the first boundary B1 forthe obstacle OB is generated by Semantic Segmentation using the CNN, andthen stored in the memory 1170, which stores any data to be accessed bythe processing module 110.

Semantic segmentation is a natural step in the progression from coarseto fine inference:

-   -   The origin could be located at classification, which consists of        making a prediction for a whole input.    -   The next step is localization/detection, which provide not only        the classes but also additional information regarding the        spatial location of those classes.    -   Finally, semantic segmentation achieves fine-grained inference        by making dense predictions inferring labels for every pixel, so        that each pixel is labeled with the class of its enclosing        object core region.

FIG. 4 shows a process of mapping each pixel in an image to an objectclass by the CNN. As shown, both vehicles in the image are labeled withthe same color (i.e., cyan), and each object class has been segmentedseparately. Namely, in FIG. 4, the white region represents the ground(i.e., free space for parking), while the red dash line represents awheel stopper.

The display 130 is provided in the vehicle SV to present various imagesto a driver under the control of the processing module 110. The imagesshown on the display 130 may include the subject vehicle SV itself,obstacles, adjacent vehicles, parking spaces, and environment, either infisheye view or birds-eye view.

FIG. 5 is a flowchart showing parking assistance procedures executed inthe preferred embodiment of the present invention. At Step S1, parkingspace search is activated by the driver of the vehicle SV when thedriver is looking for a parking space. Once the parking space search isactivated, the identification module 1110 in the processing module 110starts to detect whether there is an identifiable obstacle around thevehicle SV at Step S2. Step S2 would be repeated until an obstacle OB isidentified, for example, as an adjacent vehicle, a lamppost, a wall, acurb, or even a parking space mark etc. In such a condition, thesuccessive image frames taken by the image capture module 100 areconverted into a birds-eye view image by the image conversion module1120 in the processing module 110. Then, a first boundary B1 for theobstacle OB is generated by a Convolutional Neural Network (CNN)algorithm based on a position of the obstacle OB shown in each of thesuccessive image frames.

Hereinafter, procedures for generating the first boundary B1 areexplained. First, referring to FIG. 6a , it is a schematic view showingscanning of the obstacle OB by the moving subject vehicle SV accordingto the preferred embodiment of the present invention. When the parkingspace search is activated by the driver of the vehicle SV (Step S1 inFIG. 5), the CNN is carried out for each pixel in the pictures taken bythe image capture module 100 to determine whether there is anidentifiable feature among the pixels. If positive, the obstacle OBcontaining the identifiable feature would be identified and all thepixels which are identified to form the obstacle OB would be labeledwith the same color. In the case of FIG. 6a , the obstacle OB isidentified as an adjacent vehicle and the pixels along the edges of theobstacle OB are shown in dots, i.e., obstacle edge points. Along withthe forward travel of the vehicle SV, more pictures taken by the imagecapture module 100 (mainly by the camera 101) are analyzed and thus moreobstacle edge points are generated as shown in FIG. 6 b.

FIG. 7 shows the resultant obstacle OB having accumulated obstacle edgepoints. Since the cameras 101-104 in the image capture modules 110 usefisheye lens and the successive picture frames are taken when thecameras 101-104 are moving along with the subject vehicles SV,significant distortions of the obstacle OB are inevitable. Therefore,merging and denoising processes are needed as described below.

Firstly, each obstacle edge point from each picture frame will be movedand/or merged frame-by-frame on the basis of next obstacle edge pointfrom next picture frame. In FIG. 8a , a base score is given to a newobstacle edge point based on a distance between the new point and thecamera 101 as below:

$\begin{matrix}{{s(p)} = \frac{y}{k_{1}}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

where “s” represents a base score, “y” represents a length in thedirection opposing to the camera 101 (vertical direction as shown), “k₁”can be any natural number and “100” is used in this case.

In FIG. 8b , a base score of an old point is adjusted by adding anadjusting score which is equivalent to the base score of the new pointmultiplied by a distance factor if a first distance between the newpoint and an adjacent old point in the horizontal direction as shown issmaller than a first threshold value (t₁=50 in this case) as below:

$\begin{matrix}{s_{old}^{\prime} = {s_{old} + {\Delta \; s}}} & \left( {{{Eq}.\mspace{14mu} 2}a} \right) \\{{\Delta \; s} = {{s\left( p_{new} \right)} \times {g\left( {{y_{new} - y_{old}}} \right)}}} & \left( {{{Eq}.\mspace{14mu} 2}b} \right) \\{{g(x)} = {\max \left( {{- 1},{1 - \frac{x}{70}}} \right)}} & \left( {{{Eq}.\mspace{14mu} 2}c} \right)\end{matrix}$

where “Δs” represents the adjusting score, “Δx” represents the firstdistance, and “g” represents a distance factor.

In FIG. 8c , the adjacent old point is moved vertically if the firstdistance in the horizontal direction is smaller than a second thresholdvalue (t₂=40 in this case) and a second distance between the new pointand the adjacent old point in the vertical direction is smaller than athird threshold value (t₃=60 in this case) as below:

$\begin{matrix}{y_{old}^{\prime} = {{\left( {1 - \frac{\Delta \; s}{s_{old}^{\prime}}} \right)*y_{old}} + {\frac{\Delta \; s}{s_{old}^{\prime}}*y_{new}}}} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

Referring to FIG. 8d , the new point is merged to the adjacent old pointif the first distance is smaller than a fourth threshold value (t₄=20 inthis case) and the second distance (t₅=40 in this case) is smaller thana fifth threshold value.

After merging and denoising for the obstacle edge points in FIG. 7 withreference to FIG. 8 is performed as above, a much more precise boundaryB1 is formed as shown in FIG. 9.

Meanwhile, the ultrasonic sensors 120-125 detect distances between thevehicle SV and the obstacle OB, and the distance modification module1130 in the processing module 110 generates a second boundary B2 for theobstacle OB based on the distances between the vehicle SV and theobstacle OB. Then, the periphery definition module 1140 in theprocessing module 110 fuses the first boundary B1 and the secondboundary B2 to form a smooth periphery for the obstacle OB.

As shown in FIG. 10, it illustrates refinement of a distance between themoving vehicle and the obstacle by fusion according to the preferredembodiment of the present invention. That is, the first boundary B1 forthe obstacle OB and the second boundary B2 are fused as explained below.

In FIG. 10, any two adjacent obstacle edge points P_(sl) withcoordinates of (x_(sl), y_(sl)) and P_(sr) with coordinates of (x_(sr),y_(sr)) in the second boundary B2 are utilized. An obstacle edge pointP_(fs) with coordinates of (x_(fs), y_(fs)) in the first boundary B1having a horizontal position ranging between P_(sl) and P_(sr) isrefined according to Equation 4 as below:

$\begin{matrix}{y_{fs}^{\prime} = {{\left( {1 - \frac{x_{fs} - x_{sl}}{x_{sr} - x_{sl}}} \right) \times y_{sl}} + {\left( \frac{x_{fs} - x_{sl}}{x_{sr} - x_{sl}} \right) \times y_{sr}}}} & \left( {{Eq}.\mspace{14mu} 4} \right)\end{matrix}$

The refined result is shown in FIG. 11a . However, the refined boundaryis not sufficient to recognize whether a free space close to theobstacle OB is large enough for parking. Specifically, a periphery ofthe obstacle OB is needed for this purpose. According to the preferredembodiment, the obstacle OB is identified as an adjacent vehicle, andthe periphery would be generally rectangular in the top view.

In light of the above, an iterated linear regression directed to therefined obstacle edge points is firstly performed. The linear regressionis repeated several times each without the most distant points from theregression line. The result is shown in FIG. 11b . For parallel parkingsuch as that shown in FIG. 3, the resultant regression line in FIG. 11bis deemed to represent a side of an adjacent vehicle. Next, two linesperpendicular to the resultant regression line are formed to accommodateall the obstacle edge points between them. Thereafter, the regressionline is moved parallelly toward the subject vehicle SV to accommodateall the obstacle edge points. Therefore, a precise periphery for theobstacle OB can be easily obtained as shown in FIG. 11 c.

Returning back to FIG. 5, at step S4, the parking space recognitionmodule 1150 in the processing module 110 determines whether a potentialparking space is large enough with reference to the periphery of theobstacle OB or whether it is appropriate for parking. For instance, thepotential parking space is for the handicapped only, or the free spaceis between two parking spaces but there is a fire hydrant beyond thecurb. In particular, the identification module 1110 according to thepresent invention identifies not only identifiable features of theobstacles nearby the parking space, but also identifiable features inthe parking space, in order to determine whether a large parking spaceis appropriate for parking or not. The identifiable features in theparking space include parking space marks, colors of the parking spacemarks, handicapped or any other similar marks, NO PARKING signs, and soon.

If NO at Step S4, the flow returns to step S2 to repeat the aboveprocesses to look for another potential parking space. Otherwise, theflow goes to Step S5. At step S5, the driver can select either manualparking or self parking if the driver is not skilled in parking or theparking space is just large enough to accommodate the subject vehicle SVbut not large enough for the driver to open the driver side door.

For manual parking, the parking track module 1160 in the processingmodule 110 generates a parking track for the subject vehicle SV andsends it to the display 130 for the driver. The parking track includesan outline of the parking space and a path from a current position ofthe vehicle SV to the parking space, and it is stored in the memory 1170together with the panoramic environment around the subject vehicle SV,so that the driver can park the subject vehicle SV along the parkingpath to the parking space.

Similarly, for the self parking case, the parking track module 1160generates a parking track for the subject vehicle SV and sends it to thedisplay 130 for the driver too. In contrast with the manual parkingmentioned above, however, at this stage, the driver can activate theself parking via the display 130 or can optionally activate the selfparking via the handheld device 30 in the subject vehicle SV or outsideof the subject vehicle SV. Either the display 130 or handheld device 30can control the parking control device 20 through the processing module110 via the Controller Area Network (CAN) bus.

Referring to FIG. 12, it is a schematic view showing vehicle parkingafter a parking space is recognized according to the preferredembodiment of the present invention. The subject vehicle SV identifiestwo adjacent vehicles, parking space marks and a free space for parkingbetween the adjacent vehicles.

On the other hand, when the driver is heading for another place, he canutilize the parking track and the panoramic environment around thesubject vehicle SV stored in the memory 1170 to activate the parkingcontrol device 20 again and self-drive the subject vehicle SV to adesignated location along the stored parking track via the display 130or the handheld device 30, as shown in FIG. 13.

The above embodiment may be modified in various ways. For instance, asound output module can be incorporated into the vehicle SV to generatevarious sound messages or warnings for the driver to invite the driver'scaution, for example, when the vehicle SV is within a predetermineddistance from the obstacle OB. Furthermore, the numbers and thearrangements of the cameras and the ultrasonic sensors may be changed,depending on the needs.

While the invention has been described in terms of what is presentlyconsidered to be the most practical and preferred embodiments, it is tobe understood that the invention needs not be limited to the disclosedembodiments. On the contrary, it is intended to cover variousmodifications and similar arrangements included within the spirit andscope of the appended claims, which are to be accorded with the broadestinterpretation so as to encompass all such modifications and similarstructures.

What is claimed is:
 1. A method for recognizing a parking space for avehicle, comprising the steps of: capturing successive image framescontaining an obstacle while the vehicle is moving; identifying theobstacle from the successive image frames and generating a firstboundary for the obstacle by a Convolutional Neural Network (CNN)algorithm based on a position of the obstacle shown in each of thesuccessive image frames; detecting a plurality of distances between themoving vehicle and the obstacle; generating a second boundary for theobstacle based on the plurality of distances between the vehicle and theobstacle; defining a periphery of the obstacle by adjusting the secondboundary and the first boundary; and recognizing a parking space withthe aid of the periphery of the obstacle.
 2. A method for recognizing aparking space for a vehicle according to claim 1, wherein the obstacleis an adjacent vehicle.
 3. A method for recognizing a parking space fora vehicle according to claim 1, further comprising a step of: convertingthe successive image frames containing the obstacle into a birds-eyeview image.
 4. A method for recognizing a parking space for a vehicleaccording to claim 1, further comprising a step of: generating a parkingtrack for the vehicle.
 5. A method for recognizing a parking space for avehicle according to claim 4, further comprising a step of: steering thevehicle to the parking space with reference to the parking track.
 6. Amethod for recognizing a parking space for a vehicle according to claim1, wherein the parking space comprises an identifiable feature.
 7. Amethod for recognizing a parking space for a vehicle according to claim4, wherein the parking track comprises an outline of the parking spaceand a path from a current position of the vehicle to the parking space.8. A method for recognizing a parking space for a vehicle according toclaim 5, further comprising a step of: steering the vehicle from theparking space to a designated location along the parking track.
 9. Amethod for recognizing a parking space for a vehicle according to claim2, further comprising a step of: displaying a top view of the movingvehicle and/or a top view of the adjacent vehicle.
 10. A parkingassistance system for a vehicle, comprising: an image capture module forcapturing successive image frames containing an obstacle when thevehicle is moving; an identification module for identifying the obstaclefrom the successive image frames and generating a first boundary for theobstacle by a Convolutional Neural Network (CNN) algorithm based on aposition of the obstacle shown in each of the successive image frames;at least an ultrasonic sensor for detecting a plurality of distancesbetween the moving vehicle and the obstacle; a distance modificationmodule for generating a second boundary for the obstacle based on theplurality of distances between the vehicle and obstacle; a peripherydefinition module for defining a periphery of the obstacle by adjustingthe second boundary and the first boundary; and a parking spacerecognition module for recognizing a parking space with the aid of theperiphery of the obstacle.
 11. A parking assistance system according toclaim 10, wherein the obstacle is an adjacent vehicle.
 12. A parkingassistance system according to claim 10, further comprising an imageconversion module for converting each of the successive image framesinto a birds-eye view image.
 13. A parking assistance system accordingto claim 10, further comprising a parking track module for generating aparking track for the vehicle.
 14. A parking assistance system accordingto claim 13, further comprising a parking control device for steeringthe vehicle to the parking space with reference to the parking track.15. A parking assistance system according to claim 10, wherein theparking space comprises an identifiable feature.
 16. A parkingassistance system according to claim 13, wherein the parking trackcomprises an outline of the parking space and a path from a currentposition of the vehicle to the parking space.
 17. A parking assistancesystem according to claim 14, wherein the parking control device is usedfor steering the vehicle from the parking space to a designated locationalong the parking track.
 18. A parking assistance system according toclaim 11, further comprising a display for displaying a top view of themoving vehicle and/or a top view of the adjacent vehicle.